You bought AI.
It didn't transform anything.
$200B+ spent on enterprise AI in 2025. Most of it powers slightly better search.
The AI graveyard
Every failed enterprise AI project hits the same wall.
$500M AI Transformation
“Budget reallocated after data audit fails.”
Enterprise Chatbot v3
“Pulled after giving legally binding wrong answers.”
Automated Customer Care
“Replaced by offshore team. Again.”
Hyperscaler Partnership
“Generated one demo. Never deployed.”
30–50%
of enterprise AI projects are abandoned.
Your data doesn't understand itself
Same word. Different meaning. Every system.
| System | Customer ID field | “Active” means… | Records |
|---|---|---|---|
CRM Salesforce | CustomerID | Logged in within 90 days | 4.2M |
ERP SAP | AccountRef | Has open invoice | 4.0M |
Data Warehouse Snowflake | UserID | Any event in 12 months | 5.1M |
Support ServiceNow | TicketOwnerID | Open ticket exists | 2.8M |
4 systems. 4 definitions of “customer.” No model can reconcile this without being told explicitly.
The gap gets worse at scale
Individual
“I’ll just re-prompt.”
Minor annoyance. Minutes lost.
Team
“We all prompt differently.”
Inconsistent outputs. Hours lost.
Enterprise
“Every team gets different answers.”
Conflicting decisions. Millions lost.
It's not a better model. It's context.
AI tools
No structure
Underwhelming results
AI tools
Structured knowledge
Enterprise intelligence